Enrolment and Baseline Summaries

Exploratory summaries of enrolments and baseline data.

Load packages

library(tidyverse)
library(patchwork)
library(DT)
library(plotly)
library(knitr)
library(kableExtra)

theme_set(theme_classic(base_size = 10, base_family = "Palatino") +
  theme(panel.grid = element_blank(),
        strip.background = element_blank()))

Load data

devtools::load_all()
all_data <- read_all_no_daily()
all_daily_data <- read_all_daily()

Enrolment

Study enrolment

md <- get_interim_dates()
id <- get_intervention_dates()
p1 <- all_data %>%
  filter(ENR_rec == 1, CAssignment != "C0") %>%
  dplyr::count(RandDate) %>%
  complete(RandDate = 
             seq.Date(min(RandDate, na.rm = TRUE), 
                      as.Date("2022-04-09"), 
                      by = "1 day"),
           fill = list(n = 0)) %>%
  mutate(`Cumulative enrolments` = cumsum(n)) %>%
  rename(`Randomisation date` = RandDate) %>%
  ggplot(., aes(`Randomisation date`, `Cumulative enrolments`)) +
  geom_step() +
  geom_vline(data = md,
             aes(xintercept = as.numeric(meet_date)),
             linetype = 2) +
  labs(x = "", subtitle = "Cumulative enrolments", y = "")

p2 <- id %>%
  mutate(
    Intervention = fct_inorder(val_labels(Intervention))
  ) %>%
  ggplot(.) +
  facet_grid(Domain ~ ., drop = TRUE, scales = "free_y", space = "free_y") +
  geom_point(data = . %>% filter(Domain != "Antiviral"),
             aes(x = Intervention, y = endate), shape = 4) +
  geom_segment(
    aes(x = Intervention, xend = Intervention, 
        y = stdate, yend = endate))  +
  geom_hline(data = md,
             aes(yintercept = as.numeric(meet_date)),
             linetype = 2) +
  coord_flip() +
  labs(x = "", y = "Calendar date", subtitle = "Available interventions")

p <- p1 / p2
p

Figure 1: Cumulative enrolments to the platform, vertical lines indicate timing of interim analyses

Code

pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "enrolment-overall.pdf"), p, height = 4.5, width = 6)

Study enrolment

p1 <- all_data %>%
  filter(ENR_rec == 1, CAssignment != "C0") %>%
  dplyr::count(Country = PT_CountryName, RandDate) %>%
  complete(
    Country, 
    RandDate = seq.Date(min(RandDate, na.rm = T), as.Date("2022-04-09"), by = "1 day"),
    fill = list(n = 0)) %>%
  group_by(Country) %>%
  mutate(`Cumulative enrolments` = cumsum(n)) %>%
  ungroup() %>%
  mutate(Country = factor(Country, levels = c("India", "Australia", "Nepal", "New Zealand"))) %>%
  rename(`Randomisation date` = RandDate) %>%
  ggplot(., aes(`Randomisation date`, `Cumulative enrolments`, fill = Country)) +
  # facet_wrap( ~ Country, ncol = 1, scales = "free_y") +
  geom_area(position = "stack") +
  geom_vline(data = md,
             aes(xintercept = as.numeric(meet_date)),
             linetype = 2) +
  scale_fill_viridis_d("", begin = 0.2, end = 0.8, option = "B") +
  labs(x = "", subtitle = "Cumulative enrolments", y = "") +
  theme(legend.position = "top",
        legend.key.size = unit(0.7, "lines"),
        legend.text = element_text(size = rel(0.8)))

p3 <- all_data %>%
  filter(ENR_rec == 1, CAssignment != "C0") %>%
  dplyr::count(Country = PT_CountryName, RandDate) %>%
  complete(
    Country, 
    RandDate = seq.Date(min(RandDate, na.rm = T), as.Date("2022-04-09"), by = "1 day"),
    fill = list(n = 0)) %>%
  group_by(Country) %>%
  mutate(`Cumulative enrolments` = cumsum(n)) %>%
  ungroup() %>%
  mutate(Country = factor(Country, levels = c("India", "Australia", "Nepal", "New Zealand"))) %>%
  rename(`Randomisation date` = RandDate) %>%
  ggplot(., aes(`Randomisation date`, `Cumulative enrolments`, group = Country)) +
  # facet_wrap( ~ Country, ncol = 1, scales = "free_y") +
  geom_step(position = "stack") +
  geom_vline(data = md,
             aes(xintercept = as.numeric(meet_date)),
             linetype = 2) +
  scale_fill_viridis_d("", begin = 0.2, end = 0.8, option = "B") +
  labs(x = "", subtitle = "Cumulative enrolments", y = "") +
  theme(legend.position = "top",
        legend.key.size = unit(0.7, "lines"),
        legend.text = element_text(size = rel(0.8)))

p <- p1 / p2
p

Figure 2: Cumulative enrolments to the platform, vertical lines indicate timing of interim analyses

Code

pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "enrolment-overall-country.pdf"), p, height = 5, width = 6)

Study enrolment by country

p1 <- all_data %>%
  filter(ENR_rec == 1, CAssignment != "C0") %>%
  dplyr::count(Country = PT_CountryName, RandDate) %>%
  complete(
    Country, 
    RandDate = seq.Date(min(RandDate, na.rm = T), as.Date("2022-04-09"), by = "1 day"),
    fill = list(n = 0)) %>%
  group_by(Country) %>%
  mutate(`Cumulative enrolments` = cumsum(n)) %>%
  rename(`Randomisation date` = RandDate) %>%
  ggplot(., aes(`Randomisation date`, `Cumulative enrolments`)) +
  facet_wrap( ~ Country, ncol = 1, scales = "free_y") +
  geom_step() +
  geom_vline(data = md,
             aes(xintercept = as.numeric(meet_date)),
             linetype = 2) +
  labs(x = "", subtitle = "Cumulative enrolments", y = "")
p <- (p1 / p2) + plot_layout(heights = c(5, 2))
p

Figure 3: Cumulative enrolments to the platform by country, vertical lines indicate timing of interim analyses

Code

pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "enrolment-country.pdf"), p, height = 6, width = 6)

Study enrolment by site

p1 <- all_data %>%
  filter(ENR_rec == 1, CAssignment != "C0") %>%
  dplyr::count(Country = PT_CountryName, Site = PT_LocationName, RandDate) %>%
  complete(
    nesting(Country, Site),
    RandDate = seq.Date(min(RandDate), as.Date("2022-04-09"), by = "1 day"),
    fill = list(n = 0)) %>%
  group_by(Country, Site) %>%
  mutate(`Cumulative enrolments` = cumsum(n)) %>%
  rename(`Calendar date` = RandDate) %>%
  ggplot(., aes(`Calendar date`, `Cumulative enrolments`)) +
  facet_wrap( ~ paste(Country, Site, sep = ": "), 
              ncol = 5, scales = "free_y") +
  geom_step() +
  geom_vline(data = md,
             aes(xintercept = as.numeric(meet_date)),
             linetype = 2) +
  scale_y_continuous(breaks = function(x) 
    unique(floor(pretty(seq(0, (max(x) + 1) * 1.1)))))
ggplotly(p1, height = 1000)

Code

p_india <- all_data %>%
  filter(ENR_rec == 1, CAssignment != "C0", PT_CountryName == "India") %>%
  dplyr::count(Country = PT_CountryName, Site = PT_LocationName, RandDate) %>%
  complete(
    nesting(Country, Site),
    RandDate = seq.Date(min(RandDate), as.Date("2022-04-09"), by = "1 day"),
    fill = list(n = 0))  %>%
  group_by(Country, Site) %>%
  mutate(`Cumulative enrolments` = cumsum(n)) %>%
  rename(`Calendar date` = RandDate) %>%
  ggplot(., aes(`Calendar date`, `Cumulative enrolments`)) +
  facet_wrap( ~ Site, 
              ncol = 3, scales = "free_y") +
  geom_step() +
  geom_vline(data = md,
             aes(xintercept = as.numeric(meet_date)),
             linetype = 2) +
  scale_y_continuous(breaks = function(x) 
    unique(floor(pretty(seq(0, (max(x) + 1) * 1.1))))) +
  scale_x_date(date_breaks  = "4 months", date_labels = "%b %Y") +
  theme(strip.text = element_text(size = rel(0.7)))

Code

pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "enrolment-india-sites.pdf"), p_india, height = 5, width = 7)

Code

p_aus <- all_data %>%
  filter(ENR_rec == 1, CAssignment != "C0", PT_CountryName == "Australia") %>%
  dplyr::count(Country = PT_CountryName, Site = PT_LocationName, RandDate) %>%
  complete(
    nesting(Country, Site),
    RandDate = seq.Date(min(RandDate), as.Date("2022-04-09"), by = "1 day"),
    fill = list(n = 0))  %>%
  group_by(Country, Site) %>%
  mutate(`Cumulative enrolments` = cumsum(n)) %>%
  rename(`Calendar date` = RandDate) %>%
  ggplot(., aes(`Calendar date`, `Cumulative enrolments`)) +
  facet_wrap( ~ Site, 
              ncol = 3, scales = "free_y") +
  geom_step() +
  geom_vline(data = md,
             aes(xintercept = as.numeric(meet_date)),
             linetype = 2) +
  scale_y_continuous(breaks = function(x) 
    unique(floor(pretty(seq(0, (max(x) + 1) * 1.1))))) +
  scale_x_date(date_breaks  = "4 months", date_labels = "%b %Y") +
  theme(strip.text = element_text(size = rel(0.7)))

Code

pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "enrolment-australia-sites.pdf"), p_aus, height = 5, width = 7)

Code

p_npnz <- all_data %>%
  filter(ENR_rec == 1, CAssignment != "C0", PT_CountryName %in% c("Nepal", "New Zealand")) %>%
  dplyr::count(Country = PT_CountryName, Site = PT_LocationName, RandDate) %>%
  complete(
    nesting(Country, Site),
    RandDate = seq.Date(as.Date("2021-02-08"), as.Date("2022-04-09"), by = "1 day"),
    fill = list(n = 0))  %>%
  group_by(Country, Site) %>%
  mutate(`Cumulative enrolments` = cumsum(n)) %>%
  rename(`Calendar date` = RandDate) %>%
  ggplot(., aes(`Calendar date`, `Cumulative enrolments`)) +
  facet_wrap( ~ paste(Country, Site,  sep = ": "),
              ncol = 3, scales = "free_y") +
  geom_step() +
  geom_vline(data = md,
             aes(xintercept = as.numeric(meet_date)),
             linetype = 2) +
  scale_y_continuous(breaks = function(x) 
    unique(floor(pretty(seq(0, (max(x) + 1) * 1.1))))) +
  scale_x_date(date_breaks  = "4 months", date_labels = "%b %Y") +
  theme(strip.text = element_text(size = rel(0.7)))

Code

pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "enrolment-nepalnz-sites.pdf"), p_npnz, height = 3, width = 7)

Intervention Assignments

Code

all_data %>%
  count(FAS_ITT, ACS_ITT, CAssignment) %>%
  kable() %>%
  kable_styling("striped", font_size = 14)

FAS_ITT	ACS_ITT	CAssignment	n
1	0	C0	25
1	1	C1	619
1	1	C2	620
1	1	C3	285
1	1	C4	50
1	NA	NA	646

Table 1: Counts of intervention assignments to anticoagulation

Intervention allocations overtime

p1 <- all_data %>%
  filter(ENR_rec == 1) %>%
  count(Intervention = CAssignment, RandDate) %>%
  complete(Intervention,
           RandDate = seq.Date(min(RandDate, na.rm = T),
                               max(Sys.Date(), na.rm = T),
                               by = "1 day"),
           fill = list(n = 0)) %>%
  group_by(Intervention) %>%
  mutate(cn = cumsum(n)) %>%
  ungroup() %>%
  ggplot(., aes(RandDate, cn)) +
  geom_step(aes(colour = Intervention)) +
  scale_color_viridis_d(option = "D") +
  labs(x = "", y = "Cumulative allocation")
p1 / p2

Figure 4: Allocation to interventions by calendar date.

Country

Allocations to interventions by country of enrolment.

Allocations by site

all_data %>%
  filter(ENR_rec == 1) %>%
  count(Country = PT_CountryName, Intervention = CAssignment) %>%
  group_by(Country) %>%
  mutate(p = n / sum(n)) %>%
  mutate(lab = sprintf("%i (%.2f)", n, p)) %>%
  select(-n, -p) %>%
  ungroup() %>%
  spread(Intervention, lab, fill = "0 (0.00)") %>%
  kable(align = "lrrrrr") %>%
  kable_styling(bootstrap_options = "striped", font_size = 12)

Country	C0	C1	C2	C3	C4
Australia	23 (0.15)	51 (0.34)	60 (0.39)	7 (0.05)	11 (0.07)
India	0 (0.00)	499 (0.38)	520 (0.40)	277 (0.21)	4 (0.00)
Nepal	0 (0.00)	57 (0.47)	32 (0.27)	0 (0.00)	31 (0.26)
New Zealand	2 (0.07)	12 (0.44)	8 (0.30)	1 (0.04)	4 (0.15)

Table 2: Allocation to anti-coagulation interventions by country

Site

Allocations to interventions by site of enrolment.

Allocations by site

p <- all_data %>%
  filter(ENR_rec == 1) %>%
  count(Country = Country, 
        Site = Location,
        Intervention = CAssignment) %>%
  group_by(Site = paste(Country, Site, sep = ": ")) %>%
  mutate(p = n / sum(n)) %>%
  ggplot(., aes(Intervention, n)) +
  facet_wrap( ~ Site, scales = "free_y") +
  geom_col() +
  scale_y_continuous("Frequency",
    breaks = function(x) 
      unique(floor(pretty(seq(0, (max(x) + 1) * 1.1)))))
ggplotly(p)

Figure 5: Allocation to anti-coagulation interventions by site

Baseline Factors

The following provides an overview of the baseline covariates collected for individuals.

Demographics

Baseline demographics are summarised by intervention in the following table.

Demographics table

sdat <- all_data %>%
    filter_acs_itt() %>%
  mutate(CAssignment = factor(CAssignment, labels = intervention_labels()$CAssignment[-1]))
generate_baseline_demographics_table(sdat, format = "html")

	Anticoagulation
Variable	Low dose (n = 610)	Intermediate dose (n = 613)	Low dose with aspirin (n = 283)	Therapeutic dose (n = 50)	Overall (n = 1556)
Age (years), Median (IQR)	48 (37, 60)	48 (37, 61)	50 (38, 62)	58 (46, 69)	49 (37, 61)
Country
India, n (\%)	493 (81)	516 (84)	275 (97)	4 (8)	1288 (83)
Australia, n (\%)	49 (8)	59 (10)	7 (2)	11 (22)	126 (8)
Nepal, n (\%)	56 (9)	31 (5)	0 (0)	31 (62)	118 (8)
New Zealand, n (\%)	12 (2)	7 (1)	1 (0)	4 (8)	24 (2)
Sex
Male, n (\%)	354 (58)	387 (63)	157 (55)	25 (50)	923 (59)
Female, n (\%)	256 (42)	226 (37)	126 (45)	25 (50)	633 (41)
Weight (kg)
Median, (IQR)	68 (62, 76)	70 (62, 77)	68 (62, 76)	66 (57, 80)	69 (62, 76)
Missing, n (\%)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Vaccinated1
Yes, n (\%)	191 (31)	220 (36)	42 (15)	27 (54)	480 (31)
Missing, n (\%)	32 (5)	22 (4)	29 (10)	0 (0)	83 (5)
Ethnicity
Indian, n (\%)	494 (81)	518 (85)	275 (97)	4 (8)	1291 (83)
European, n (\%)	21 (3)	18 (3)	4 (1)	4 (8)	47 (3)
Asian, n (\%)	20 (3)	12 (2)	1 (0)	10 (20)	43 (3)
Pacific Islander, n (\%)	13 (2)	12 (2)	2 (1)	3 (6)	30 (2)
Middle Eastern, n (\%)	11 (2)	11 (2)	0 (0)	0 (0)	22 (1)
Maori, n (\%)	3 (0)	4 (1)	0 (0)	3 (6)	10 (1)
African, n (\%)	1 (0)	0 (0)	1 (0)	0 (0)	2 (0)
Aboriginal, n (\%)	0 (0)	1 (0)	0 (0)	1 (2)	2 (0)
Latin American, n (\%)	0 (0)	1 (0)	0 (0)	0 (0)	1 (0)
Other, n (\%)	45 (7)	28 (5)	0 (0)	23 (46)	96 (6)
Unknown, n (\%)	8 (1)	9 (1)	0 (0)	3 (6)	20 (1)
Smoking
Current, n (\%)	17 (3)	21 (3)	3 (1)	5 (10)	46 (3)
Former, n (\%)	74 (12)	53 (9)	15 (5)	14 (28)	156 (10)
Never, n (\%)	519 (85)	539 (88)	265 (94)	31 (62)	1354 (87)
Missing, n (\%)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
1 Site LUD did not have ethics approval for collection of vaccination status.

Table 3: Baseline demographics for participants randomised into the anticoagulation domain.

Save table to outputs

save_tex_table(
  generate_baseline_demographics_table(sdat, format = "latex"),
  "baseline/demographics"
)

Co-morbidities

Baseline co-morbidities are summarised by anti-coagulation intervention in the following table.

Co-morbidities table

sdat <- all_data %>%
    filter_acs_itt() %>%
  mutate(CAssignment = factor(CAssignment, labels = intervention_labels()$CAssignment[-1]))
generate_baseline_comorbidities_table(sdat, format = "html")

	Anticoagulation
Comorbidity	Low dose (n = 610)	Intermediate dose (n = 613)	Low dose with aspirin (n = 283)	Therapeutic dose (n = 50)	Overall (n = 1556)
None, n (\%)	364 ( 60)	378 ( 62)	166 ( 59)	19 ( 38)	927 ( 60)
Hypertension, n (\%)	147 ( 24)	140 ( 23)	68 ( 24)	14 ( 28)	369 ( 24)
Diabetes, n (\%)	140 ( 23)	139 ( 23)	78 ( 28)	11 ( 22)	368 ( 24)
Obesity, n (\%)	23 ( 4)	22 ( 4)	3 ( 1)	5 ( 10)	53 ( 3)
Asthma, n (\%)	19 ( 3)	16 ( 3)	6 ( 2)	4 ( 8)	45 ( 3)
Chronic lung disease, n (\%)	16 ( 3)	13 ( 2)	1 ( 0)	7 ( 14)	37 ( 2)
Chronic cardiac disease, n (\%)	11 ( 2)	15 ( 2)	1 ( 0)	2 ( 4)	29 ( 2)
Obstructive sleep apnoea, n (\%)	3 ( 0)	2 ( 0)	2 ( 1)	0 ( 0)	7 ( 0)
Iatrogenic immunosuppression, n (\%)	1 ( 0)	6 ( 1)	0 ( 0)	0 ( 0)	7 ( 0)
Chronic kidney disease, n (\%)	0 ( 0)	5 ( 1)	1 ( 0)	0 ( 0)	6 ( 0)
Malignant neoplasm, n (\%)	1 ( 0)	2 ( 0)	0 ( 0)	1 ( 2)	4 ( 0)
Moderate or severe liver disease, n (\%)	2 ( 0)	1 ( 0)	0 ( 0)	0 ( 0)	3 ( 0)
Dialysis, n (\%)	0 ( 0)	1 ( 0)	0 ( 0)	0 ( 0)	1 ( 0)
HIV infection, n (\%)	1 ( 0)	0 ( 0)	0 ( 0)	0 ( 0)	1 ( 0)
Dementia, n (\%)	0 ( 0)	0 ( 0)	0 ( 0)	0 ( 0)	0 ( 0)
Missing, n (\%)	0 ( 0)	0 ( 0)	0 ( 0)	0 ( 0)	0 (0.0)

Table 4: Baseline co-morbidities for participants randomised into then anticoagulation domain.

Save table to outputs

save_tex_table(
  generate_baseline_comorbidities_table(sdat, format = "latex"),
  "baseline/comorbidities"
)

Prognostics

Baseline prognostics are summarised by anti-coagulation intervention in the following table.

Prognostics table

sdat <- all_data %>%
    filter_acs_itt() %>%
  mutate(CAssignment = factor(CAssignment, labels = intervention_labels()$CAssignment[-1]))
generate_baseline_prognostics_table(sdat, format = "html")

	Anticoagulation
Variable	Low dose (n = 610)	Intermediate dose (n = 613)	Low dose with aspirin (n = 283)	Therapeutic dose (n = 50)	Overall (n = 1556)
Was the patient on room air for any of the preceding 24 hours?
Yes, n (\%)	460 (75)	460 (75)	224 (79)	39 (78)	1183 (76)
Missing, n (\%)	8 (1)	9 (1)	8 (3)	0 (0)	25 (2)
Was the patient's GCS < 15?
Yes, n (\%)	63 (10)	65 (11)	6 (2)	2 (4)	136 (9)
Missing, n (\%)	125 (20)	135 (22)	60 (21)	0 (0)	320 (21)
Peripheral oxygen saturation (SpO2) on room air (Lowest)
Median (IQR)	95 (94, 97)	96 (94, 97)	96 (94, 97)	94 (92, 96)	96 (94, 97)
Missing, n (\%)	150 (25)	153 (25)	59 (21)	11 (22)	373 (24)
Highest respiratory rate (breaths/minute)
Median (IQR)	22 (21, 25)	22 (21, 26)	22 (20, 26)	22 (20, 24)	22 (21, 26)
Missing, n (\%)	0 (0)	1 (0)	0 (0)	0 (0)	1 (0)
Highest recorded Urea in the last 24 hours (mmol/L)
Median (IQR)	4 (3, 5)	5 (4, 6)	4 (3, 6)	4 (3, 6)	4 (3, 6)
Missing, n (\%)	30 (5)	33 (5)	16 (6)	1 (2)	80 (5)
Highest recorded CRP in the last 24 hours (mg/L)
Median (IQR)	70 (37, 190)	75 (38, 220)	77 (44, 223)	68 (33, 129)	73 (39, 200)
Missing, n (\%)	74 (12)	59 (10)	18 (6)	29 (58)	180 (12)
APTT\\textsuperscript{1}
Median (IQR)	33 (29, 36)	33 (30, 36)	32 (28, 37)	33 (28, 38)	33 (29, 36)
Missing, n (\%)	430 (70)	439 (72)	195 (69)	35 (70)	1099 (71)
INR\\textsuperscript{1}
Mean (SD)	1.19 (0.39)	1.23 (0.58)	1.32 (1.34)	1.12 (0.18)	1.23 (0.72)
Missing, n (\%)	103 (17)	105 (17)	47 (17)	7 (14)	262 (17)
Fibrinogen\\textsuperscript{1} (g/L)
Mean (SD)	5.19 (2.01)	5.20 (1.60)	4.75 (1.40)	6.49 (1.52)	5.14 (1.71)
Missing, n (\%)	564 (92)	555 (91)	243 (86)	42 (84)	1404 (90)
Prothrombin time\\textsuperscript{1} (sec)
Median (IQR)	14 (13, 17)	14 (13, 17)	15 (13, 16)	13 (12, 14)	14 (13, 16)
Missing, n (\%)	193 (32)	204 (33)	116 (41)	10 (20)	523 (34)
Taking aspirin
Yes, n (\%)	20 (3)	25 (4)	2 (1)	3 (6)	50 (3)
Missing, n (\%)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Time from onset of symptoms to hospitalisation
Median (IQR)	5 (3, 7)	5 (3, 6)	4 (2, 6)	4 (3, 6)	4 (3, 6)
Time from hospitalisation to randomisation
Median (IQR)	1 (0, 2)	1 (0, 2)	1 (1, 2)	1 (1, 1)	1 (0, 2)
1 For APTT, INR, Fibrinogen, and Prothrombin only at least one required.

Table 5: Baseline prognostic variables for participants randomised into anticoagulation domain.

Save table to outputs

save_tex_table(
  generate_baseline_prognostics_table(sdat, format = "latex"),
  "baseline/prognostics"
)

Age

Histogram of age

p_age <- all_data %>%
  filter_acs_itt() %>%
  ggplot(., aes(AgeAtEntry)) + 
  geom_histogram(
    breaks = c(18, seq(20, 100, 5)), 
    colour = "white", 
    closed="left") +
  labs(
    x = "Age at randomisation (5-year bins, 30 to 34, 35 to 39, etc.)", 
    y = "Count") +
  scale_x_continuous(breaks = seq(20, 95, 5))
p_age

Figure 6: Distribution of age

Code

pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "age-overall.pdf"), p_age, height = 2, width = 4)

Histogram of age by anti-coagulation intervention

p <- all_data %>%
  filter_acs_itt() %>%
  ggplot(., aes(AgeAtEntry)) + 
  facet_wrap( ~ CAssignment, scales = "free_y", ncol = 2) +
  geom_histogram(
    breaks = c(18, seq(20, 100, 5)), 
    colour = "white", 
    closed="left") +
  labs(
    x = "Age at randomisation (5-year bins, 30 to 34, 35 to 39, etc.)", 
    y = "Count") +
  scale_x_continuous(breaks = seq(20, 95, 5))
p

Figure 7: Distribution of age by anti-coagulation intervention

Weight

Histogram of weight

p <- all_data %>%
  filter_acs_itt() %>%
  filter(!is.na(BAS_WeightMeasurement)) %>%
  # 2 with weight reported as 0, make NA
  mutate(BAS_Weight = if_else(BAS_Weight == 0, NA_real_, BAS_Weight)) %>%
  ggplot(., aes(BAS_Weight)) +
  facet_wrap( ~ BAS_WeightMeasurement, scales = "free_y") +
  geom_histogram()
p

Figure 8: Distribution of weight

Oxygen

Code

all_data %>% 
  dplyr::count(
    BAS_OnRoomAir24hrs,
    BAS_OnRoomAir24hrsUnknown,
    is.na(BAS_PeripheralOxygen)
  )

# A tibble: 4 × 4
  BAS_OnRoomAir24hrs BAS_OnRoomAir24hrsUnknown `is.na(BAS_PeripheralOxyg…`     n
  <chr>              <chr>                     <lgl>                       <int>
1 No                 No                        TRUE                          365
2 Yes                No                        FALSE                        1209
3 <NA>               Yes                       TRUE                           25
4 <NA>               <NA>                      TRUE                          646

Code

all_data %>%
  dplyr::count(
    BAS_OnRoomAir24hrs == "Yes",
    BAS_PeripheralOxygen < 94
  )

# A tibble: 4 × 3
  `BAS_OnRoomAir24hrs == "Yes"` `BAS_PeripheralOxygen < 94`     n
  <lgl>                         <lgl>                       <int>
1 FALSE                         NA                            365
2 TRUE                          FALSE                         921
3 TRUE                          TRUE                          288
4 NA                            NA                            671

Date since first symptoms

Code

pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "days-between-events-overall.pdf"), p, height = 3, width = 6)

Code

tsfs_dat %>%
  dplyr::count(CAssignment, `TSFS > 7 days` = fs_to_rand > 7) %>%
  spread(CAssignment, n)

# A tibble: 2 × 5
  `TSFS > 7 days`    C1    C2    C3    C4
  <lgl>           <int> <int> <int> <int>
1 FALSE             421   432   206    39
2 TRUE              189   181    77    11

D-dimer

Code

all_data %>%
  filter_acs_itt() %>%
  dplyr::count(
    BAS_DDimerTestPerformed,
    BAS_DDimerOutOfRange, 
    BAS_DDimerUnits)

# A tibble: 7 × 4
  BAS_DDimerTestPerformed BAS_DDimerOutOfRange BAS_DDimerUnits     n
  <chr>                   <chr>                <chr>           <int>
1 No                      <NA>                 ug/mL OR mg/L       1
2 No                      <NA>                 <NA>              283
3 Yes                     No                   ug/L OR ng/mL     287
4 Yes                     No                   ug/mL OR mg/L     551
5 Yes                     Yes                  ug/L OR ng/mL     215
6 Yes                     Yes                  ug/mL OR mg/L     217
7 Yes                     <NA>                 ug/mL OR mg/L       2

Code

all_data %>% 
  filter_acs_itt() %>%
  ggplot(., aes(BAS_DDimerEntered, BAS_DDimerResult)) +
  facet_wrap( ~ BAS_DDimerUnits, scales = "free_x") +
  geom_point()

Code

all_data %>% 
  filter_acs_itt() %>%
  ggplot(., aes(BAS_DDimerResult)) +
  geom_histogram() +
  scale_x_log10()

Serum Creatinine

There is an issue with the serum creatinine units for some participants. These have been reported as “umol/L” when in fact it is more likely they are in mg/dL (spike at values reported as umol/L and less than 1, noting that 1 mg/dL = 88.42 umol/L).

Code

all_data %>%
  filter(
    EL_SerumCreatinineUnits == "umol/L",
    EL_SerumCreatinineBlood <= 1.5
  ) %>%
  select(
    EligibilityCode, 
    StudyPatientID, 
    starts_with("EL_Serum"))

# A tibble: 0 × 7
# … with 7 variables: EligibilityCode <chr>, StudyPatientID <chr>,
#   EL_SerumCreatinineBlood <dbl>, EL_SerumCreatinineUnits <chr>,
#   EL_SerumCreatinine_umolL <dbl>, EL_SerumPotassium <dbl>,
#   EL_SerumSodium <dbl>

Code

  # write_csv(file.path(ASCOT_DATA, "derived", "low_creatinine_values.csv"))
ggplot(all_data %>% 
         filter(!is.na(EL_SerumCreatinineUnits)), 
       aes(EL_SerumCreatinine_umolL)) +
  geom_histogram(bins = 25, boundary = 0) +
  facet_wrap( ~ EL_SerumCreatinineUnits) +
  scale_x_log10()

Figure 9: Histogram of serum creatinine levels at baseline by reported units. Note the cluster at ~ 1 umol/L for values reported in umol/L.

Code

tmp <- all_data %>%
  filter(!is.na(EL_SerumCreatinineUnits)) %>%
  mutate(
    clearance = creatinine_clearance(
      Sex, AgeAtEntry, BAS_Weight, EL_SerumCreatinine_umolL)
  )
p1 <- ggplot(tmp, aes(clearance)) + 
  facet_wrap( ~ EL_SerumCreatinineUnits) +
  geom_histogram() + 
  geom_vline(xintercept = 30) +
  scale_x_log10() +
  labs(x = "Creatinine clearance\n(mL/min)", y = "Frequency")
p2 <- ggplot(tmp, aes(EL_SerumCreatinine_umolL, clearance)) + 
  facet_wrap( ~ EL_SerumCreatinineUnits) +
  geom_point() + 
  scale_x_log10() +
  scale_y_log10() +
  labs(x = "Serum creatinine\n(umol/L)", y = "Creatinine clearance\n(mL/min)")
p1 / p2

Figure 10: Creatinine clearance rate from formula. Facets are the reported units (converted to umol/L where required). Vertical line marks 30 mL/min.

Drugs Received During Hospital Stay

Drugs during hospital stay table

sdat <- all_data %>%
    filter_acs_itt() %>%
  mutate(CAssignment = factor(CAssignment, labels = intervention_labels()$CAssignment[-1]))
generate_discharge_drugs_table(sdat, format = "html")

	Anticoagulation
Drug received	\makecell[c]{Low dose\\(n = 610)}	\makecell[c]{Intermediate dose\\(n = 613)}	\makecell[c]{Low dose with aspirin\\(n = 283)}	\makecell[c]{Therapeutic dose\\(n = 50)}	\makecell[c]{Overall\\(n = 1556)}
Antibacterial drugs, n (\%)	449 (74)	443 (72)	231 (82)	38 (76)	1161 (75)
Antivirals
No antiviral, n (\%)	125 (20)	120 (20)	44 (16)	39 (78)	328 (21)
Camostat, n (\%)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Favipiravir, n (\%)	97 (16)	100 (16)	34 (12)	0 (0)	231 (15)
Doxycycline, n (\%)	76 (12)	81 (13)	22 (8)	2 (4)	181 (12)
Ivermectin, n (\%)	201 (33)	200 (33)	91 (32)	0 (0)	492 (32)
Remdesivir, n (\%)	286 (47)	289 (47)	169 (60)	10 (20)	754 (48)
Other antiviral, n (\%)	3 (0)	1 (0)	1 (0)	0 (0)	5 (0)
Immunomodulatory
No immunomodulatory, n (\%)	60 (10)	68 (11)	38 (13)	6 (12)	172 (11)
Anakinra, n (\%)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Corticosteroids, n (\%)	396 (65)	388 (63)	183 (65)	37 (74)	1004 (65)
Sarilumab, n (\%)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Azithromycin, n (\%)	105 (17)	108 (18)	38 (13)	5 (10)	256 (16)
Tocilizumab, n (\%)	12 (2)	11 (2)	5 (2)	2 (4)	30 (2)
Baricitinib, n (\%)	36 (6)	50 (8)	11 (4)	6 (12)	103 (7)
Ruxolitinib, n (\%)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Tofacitinib, n (\%)	11 (2)	13 (2)	5 (2)	0 (0)	29 (2)
Zinc, n (\%)	388 (64)	394 (64)	173 (61)	13 (26)	968 (62)
Other immunomodulatory, n (\%)	20 (3)	20 (3)	11 (4)	1 (2)	52 (3)

Table 6: Drugs received during hospital stay for participants randomised into the anticoagulation domain.

Save table to outputs

save_tex_table(
  generate_discharge_drugs_table(sdat, format = "latex"),
  "baseline/drugs"
)